Inproceedings,
Estimating query difficulty for news prediction retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management, page 2623--2626. New York, NY, USA, ACM, (2012)
DOI: 10.1145/2396761.2398707
Abstract
News prediction retrieval has recently emerged as the task of retrieving <i>predictions</i> related to a given news story (or a query). Predictions are defined as sentences containing time references to future events. Such future-related information is crucially important for understanding the temporal development of news stories, as well as strategies planning and risk management. The aforementioned work has been shown to retrieve a significant number of relevant predictions. However, only a certain news topics achieve good retrieval effectiveness. In this paper, we study how to determine the difficulty in retrieving predictions for a given news story. More precisely, we address the <i>query difficulty estimation</i> problem for news prediction retrieval. We propose different entity-based predictors used for classifying queries into two classes, namely, <i>Easy</i> and <i>Difficult</i>. Our prediction model is based on a machine learning approach. Through experiments on real-world data, we show that our proposed approach can predict query difficulty with high accuracy.
